{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_blobs(n_samples=1000,centers=3,n_features=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.90199764, -3.52956243],\n",
       "       [-1.98393837, -3.57768991],\n",
       "       [-9.70183114, -6.02341839],\n",
       "       ...,\n",
       "       [-9.65961562, -6.01237169],\n",
       "       [-8.61886216, -6.1579854 ],\n",
       "       [-2.74729721, -5.85448802]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 0, 1, 0, 1, 0, 2, 1,\n",
       "       2, 2, 2, 2, 1, 1, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 2, 0,\n",
       "       1, 2, 0, 2, 2, 2, 0, 0, 1, 0, 2, 0, 1, 0, 1, 2, 0, 0, 1, 2, 0, 2,\n",
       "       1, 2, 0, 0, 0, 2, 2, 1, 0, 1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 0, 2, 0,\n",
       "       2, 2, 0, 2, 0, 1, 0, 2, 2, 0, 1, 1, 0, 2, 0, 2, 1, 1, 2, 0, 2, 2,\n",
       "       0, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 1, 0, 1, 1, 2, 0, 2, 2, 1, 0, 2,\n",
       "       2, 0, 0, 2, 2, 0, 1, 1, 0, 0, 0, 2, 2, 1, 1, 0, 0, 1, 2, 1, 2, 0,\n",
       "       1, 1, 1, 1, 0, 0, 2, 0, 1, 0, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 0, 2,\n",
       "       0, 0, 2, 1, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0, 2, 2, 1, 1, 0, 0,\n",
       "       0, 1, 0, 2, 1, 2, 2, 2, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0,\n",
       "       1, 1, 0, 2, 2, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 1, 0, 2, 0, 1, 1, 2,\n",
       "       0, 2, 0, 1, 0, 1, 0, 1, 1, 0, 1, 2, 1, 0, 0, 2, 1, 1, 2, 1, 1, 2,\n",
       "       1, 0, 2, 1, 1, 0, 0, 2, 2, 1, 1, 0, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 0, 2, 2, 1, 2, 1, 0, 1, 2, 1, 0, 1, 2, 2, 1, 1, 0, 2,\n",
       "       2, 2, 1, 2, 2, 0, 2, 1, 0, 0, 1, 0, 0, 1, 1, 0, 2, 1, 0, 1, 2, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 2, 2, 2, 2, 1, 1, 0, 2, 1, 1, 0, 1, 1, 2,\n",
       "       2, 1, 2, 2, 1, 0, 0, 2, 0, 2, 0, 0, 2, 0, 1, 1, 0, 1, 1, 0, 2, 1,\n",
       "       2, 1, 0, 1, 0, 2, 2, 0, 2, 2, 0, 1, 0, 2, 2, 0, 1, 1, 2, 0, 1, 2,\n",
       "       1, 1, 0, 0, 1, 2, 1, 2, 1, 2, 0, 0, 2, 1, 1, 2, 0, 2, 0, 2, 2, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2,\n",
       "       2, 1, 0, 1, 2, 1, 2, 1, 1, 0, 0, 1, 0, 0, 1, 2, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1, 2, 0, 0, 2, 0, 1, 2, 0, 0, 2, 1,\n",
       "       1, 2, 2, 0, 0, 2, 2, 1, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 2, 1,\n",
       "       0, 0, 0, 2, 0, 2, 0, 1, 2, 2, 1, 0, 2, 0, 2, 1, 2, 2, 2, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 2, 0, 1, 1, 1, 2, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 2,\n",
       "       1, 2, 2, 0, 1, 2, 1, 0, 2, 1, 2, 2, 1, 1, 1, 0, 2, 1, 1, 0, 2, 1,\n",
       "       0, 2, 0, 1, 2, 2, 1, 1, 2, 1, 0, 0, 2, 2, 2, 1, 1, 2, 0, 0, 0, 2,\n",
       "       0, 1, 0, 2, 0, 0, 0, 2, 1, 0, 2, 2, 1, 2, 1, 2, 1, 1, 2, 0, 0, 1,\n",
       "       1, 1, 2, 1, 1, 0, 1, 0, 0, 0, 0, 2, 1, 0, 0, 0, 1, 2, 1, 2, 2, 0,\n",
       "       1, 2, 2, 0, 2, 1, 2, 2, 2, 0, 1, 2, 1, 2, 2, 0, 0, 1, 0, 2, 1, 2,\n",
       "       1, 1, 0, 0, 0, 1, 1, 2, 0, 0, 2, 1, 2, 2, 2, 2, 0, 2, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 0, 0, 1, 2, 2, 1, 2, 0, 0,\n",
       "       1, 1, 0, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 0, 2, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0, 1, 0, 0, 2, 1,\n",
       "       1, 2, 1, 1, 0, 2, 1, 0, 2, 1, 2, 0, 0, 0, 0, 2, 0, 2, 1, 1, 2, 1,\n",
       "       0, 1, 2, 2, 0, 1, 2, 0, 0, 2, 1, 2, 1, 2, 2, 0, 1, 2, 2, 0, 0, 1,\n",
       "       2, 1, 2, 0, 2, 2, 2, 2, 0, 0, 0, 1, 0, 2, 1, 1, 2, 2, 2, 0, 2, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 1, 1, 2, 0, 1, 0, 1, 0, 0, 2, 1,\n",
       "       1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 2, 2, 1, 0, 2, 2, 1, 1, 0, 2, 1,\n",
       "       0, 2, 1, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1,\n",
       "       1, 0, 0, 1, 0, 0, 2, 0, 2, 0, 2, 1, 2, 2, 1, 0, 0, 0, 1, 2, 0, 2,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 1, 1, 2, 2, 0,\n",
       "       1, 1, 0, 2, 1, 0, 2, 0, 2, 0, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 0, 1,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2, 1, 2, 2, 1, 2, 2, 0, 1,\n",
       "       2, 1, 1, 2, 2, 0, 2, 2, 2, 0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22737c46d60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## standardization--feature scaling technique\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled=scaler.fit_transform(X_train)\n",
    "X_test_scaled=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Elbow method To select K Value\n",
    "wcss=[]\n",
    "for k in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=k,init=\"k-means++\")\n",
    "    kmeans.fit(X_train_scaled)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1340.0000000000023,\n",
       " 571.2724981447043,\n",
       " 129.06920301404648,\n",
       " 112.8929467989705,\n",
       " 98.43762944408469,\n",
       " 84.2587068347742,\n",
       " 75.1016410696618,\n",
       " 66.33725974517868,\n",
       " 58.10165910172263,\n",
       " 52.30910767336157]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot elbow curve\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.xticks(range(1,11))\n",
    "plt.xlabel(\"Number of Clustrers\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans=KMeans(n_clusters=3,init=\"k-means++\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 2, 1, 2, 1, 0, 1, 1, 2, 2, 2, 2, 2, 1, 0, 1, 1, 1, 1, 0,\n",
       "       2, 1, 1, 2, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 1, 0, 1, 1, 0,\n",
       "       2, 1, 0, 0, 2, 0, 1, 0, 1, 0, 1, 2, 0, 2, 2, 1, 1, 2, 2, 2, 2, 0,\n",
       "       2, 0, 2, 2, 2, 0, 1, 2, 1, 2, 0, 1, 1, 2, 1, 0, 1, 1, 1, 1, 0, 2,\n",
       "       1, 1, 0, 0, 1, 0, 2, 2, 0, 1, 0, 0, 2, 2, 1, 0, 1, 2, 0, 0, 2, 2,\n",
       "       2, 0, 2, 1, 2, 2, 0, 1, 0, 2, 0, 2, 1, 1, 2, 2, 1, 1, 0, 0, 1, 0,\n",
       "       2, 0, 1, 0, 1, 2, 0, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0, 2, 1, 0, 2,\n",
       "       2, 1, 2, 2, 0, 1, 0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 2, 2, 0, 0, 0, 1, 1, 2, 0, 2, 0, 2, 2, 1, 2, 2, 0, 0, 0, 1, 2,\n",
       "       0, 0, 0, 1, 1, 0, 2, 1, 1, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 1, 1,\n",
       "       2, 0, 0, 1, 0, 2, 0, 1, 1, 0, 0, 1, 2, 2, 2, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 2, 1, 2, 2, 0, 0, 2, 1, 1, 2, 2, 0, 2, 2,\n",
       "       0, 0, 1, 2, 0, 1, 1, 2, 1, 2, 2, 2, 0, 1, 1, 2, 2, 1, 0, 1, 2, 1,\n",
       "       2, 1, 2, 2, 1, 0, 0, 0, 0, 2, 2, 2, 0, 1, 2, 1, 0, 1, 1, 2, 0, 2,\n",
       "       2, 1, 0, 2, 1, 2, 2, 0, 0, 1, 0, 1, 0, 1, 1, 1, 2, 2, 1, 2, 0, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 0, 1, 1, 1, 2, 2,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 0, 2, 2, 2, 2, 0, 0, 1, 2, 2, 0, 2, 2, 0,\n",
       "       0, 0, 1, 0, 2, 1, 1, 1, 1, 0, 2, 1, 0, 0, 2, 2, 2, 1, 0, 2, 2, 1,\n",
       "       1, 1, 0, 2, 2, 1, 1, 0, 1, 1, 0, 0, 0, 2, 1, 0, 2, 0, 1, 0, 2, 0,\n",
       "       2, 1, 0, 2, 1, 2, 1, 1, 0, 2, 0, 1, 2, 1, 0, 2, 2, 2, 2, 2, 1, 1,\n",
       "       2, 1, 0, 1, 2, 2, 0, 1, 1, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1, 0, 0, 0,\n",
       "       2, 0, 0, 2, 1, 2, 1, 0, 1, 0, 2, 2, 1, 0, 1, 1, 1, 1, 2, 0, 0, 2,\n",
       "       0, 0, 2, 0, 2, 1, 1, 0, 2, 1, 2, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 2,\n",
       "       0, 1, 1, 2, 2, 1, 0, 0, 0, 0, 2, 1, 1, 2, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 0, 1, 1, 2, 0, 1, 1, 2, 1, 1, 2, 0,\n",
       "       0, 1, 1, 1, 2, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 1, 2, 2, 2, 1, 0, 2, 2, 0, 1, 0, 1, 1, 0, 1, 1, 2, 1, 2, 2,\n",
       "       2, 0, 1, 2, 1, 0, 0, 1, 0, 1, 2, 0, 0, 1, 1, 0, 1, 0, 1, 2, 1, 2,\n",
       "       2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 1, 2, 1, 0, 1, 2, 2, 1, 1, 0, 2, 1,\n",
       "       2, 0, 1, 1, 1, 2, 0, 1, 0, 0, 1, 2, 1, 1, 0, 0, 0, 1, 1, 2, 0, 2,\n",
       "       0, 1, 2, 0, 2, 2, 1, 1, 2, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit_predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=kmeans.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 0, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 1, 0, 0, 1, 2, 0, 0, 1,\n",
       "       0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 2, 1, 2, 1, 2,\n",
       "       2, 1, 1, 0, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 2, 2, 0, 1, 2,\n",
       "       2, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 2, 0, 0, 2, 0, 2, 2, 1, 0, 2, 1,\n",
       "       2, 0, 1, 0, 1, 0, 2, 1, 1, 2, 2, 1, 0, 0, 0, 2, 1, 0, 1, 2, 2, 1,\n",
       "       1, 1, 0, 2, 2, 0, 1, 1, 1, 2, 2, 2, 2, 1, 0, 0, 2, 1, 2, 1, 2, 2,\n",
       "       2, 1, 1, 1, 1, 1, 2, 2, 0, 1, 2, 0, 2, 2, 2, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 2, 0, 2, 1, 1, 2, 1, 2, 0, 1, 2, 0, 2, 2, 0, 2, 1, 1, 0, 2, 2,\n",
       "       2, 2, 0, 0, 0, 0, 2, 2, 1, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
       "       1, 1, 2, 0, 2, 0, 1, 1, 2, 1, 2, 1, 2, 1, 0, 2, 2, 2, 2, 2, 1, 2,\n",
       "       0, 0, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 0, 1, 1, 1, 1, 0, 2, 2, 0, 2,\n",
       "       2, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 2, 2, 1, 2, 2, 0, 1, 2, 2, 1, 2, 1, 0, 1, 1, 1, 0, 2, 0, 1, 1,\n",
       "       0, 0, 2, 0, 0, 1, 2, 1, 2, 2, 1, 1, 2, 0, 1, 1, 1, 0, 2, 2, 2, 0,\n",
       "       2, 0, 1, 1, 0, 1, 1, 0, 2, 1, 1, 1, 2, 1, 0, 0, 1, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2273f345c40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test[:,0],X_test[:,1],c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Validating the k value\n",
    "## kneelocator\n",
    "## Silhoutee scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kneed in c:\\users\\win10\\anaconda3\\lib\\site-packages (0.8.1)\n",
      "Requirement already satisfied: numpy>=1.14.2 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from kneed) (1.19.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from kneed) (1.5.2)\n"
     ]
    }
   ],
   "source": [
    "## kneelocator\n",
    "!pip install kneed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kneed import KneeLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "kl=KneeLocator(range(1,11),wcss,curve=\"convex\",direction=\"decreasing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kl.elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## silhoutte score\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_coefficients=[]\n",
    "for k in range(2,11):\n",
    "    kmeans=KMeans(n_clusters=k,init=\"k-means++\")\n",
    "    kmeans.fit(X_train_scaled)\n",
    "    score=silhouette_score(X_train_scaled,kmeans.labels_)\n",
    "    silhouette_coefficients.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5791208264052049,\n",
       " 0.7345149600137074,\n",
       " 0.590061277437689,\n",
       " 0.46306511562346064,\n",
       " 0.3194384017662996,\n",
       " 0.3260433893207664,\n",
       " 0.3295975251169475,\n",
       " 0.33870715403767093,\n",
       " 0.33711491411125977]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting silhouette score\n",
    "plt.plot(range(2,11),silhouette_coefficients)\n",
    "plt.xticks(range(2,11))\n",
    "plt.xlabel(\"Number of Cluters\")\n",
    "plt.ylabel(\"Silhoutte Coeffecient\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
